SIM UNIVERSITY
SCHOOL OF SCIENCE AND TECHNOLOGY
QUANTITATIVE ASSESSMENT OF RIGHT
VENTRICULAR REGIONAL WALL MOTION IN
HUMAN HEART
STUDENT: THARAPHE KHINE ZAR, CHRISTINA
(PI NO. Z0605636)
SUPERVISOR: DR ZHONG LIANG
PROJECT CODE: JUL2009/ July2009/BME/015
A project report submitted to SIM University in partial fulfilment of the
requirements for Bachelor of Science in Biomedical engineering the degree of
Bachelor of Engineering
May 2010
1
Abstract
BACKGROUND: The importance of right ventricular (RV) dysfunction is increasingly
recognized in multiple cardiopulmonary diseases such as pulmonary arterial hypertension,
congestive heart failure and myocardial infarction. However, the assessment of RV function
remains limited and challenging due to it’s complexity of geometry and the mechanical
interaction with left ventricle (LV).
MATERIAL AND METHOD: 9 subjects who underwent magnetic resonance imaging (MRI)
scans were recruited for this study. 4 subjects are healthy normal volunteers (female/male=1/3,
aging from 16 to 29 years old) without any major medical problem while the other 5 are patients
with right ventricular (RV) dysfunction (female/male=2/3, aging from 14 to 60 years old). The
contours of right ventricles were drawn using CMRtools during cardiac cycle. A MATLAB
algorithm was developed to calculate the displacements values during the cardiac cycle to access
the wall motion of RV.
RESULTS: The algorithm for automatic quantitative assessment of RV wall motion in terms of
displacement was developed. There were distinct differences in regional wall displacement in
patients with RV dysfunction (PRV) compared to normal healthy volunteers (NRV). The right
ventricular shape was elongated, cresentic and trapezoidal in NRV. However, for PRV, crosssectional area was significantly larger and they are highly dilated or round compared to normal
subjects. It was also observed that displacement waveform for PRV have more
variations/multiple high peaks compared to NRV. Maximal displacement was much lower in
PRV compared to NRV, in particular at basal regions (0.21±0.06 mm in PRV versus 0.38±0.07
mm in NRV).
CONCLUSION: The Matlab-based algorithm for automatic quantitative assessment of right
ventricular regional wall motion has been developed. There were distinctive differences in wall
motion in patients with RV dysfunction compared to normal subjects. This new approach may
facilitate the heart disease diagnostic and management. It was also useful to evaluate the
effectiveness of therapeutic intervention in patient with severe right ventricular failure.
2
Acknowledgement
I would like to acknowledge and extend my heartfelt gratitude to my supervisor, Dr. Zhong
Liang, whose encouragement, guidance and support from the initial to the final level enabled me
to develop an understanding of the subject.
I would also like to offer my gratitude to the Divine, and my regards and blessings to my family,
friends and those who supported me in any respect during the completion of this project.
Christina Tharaphe Khine Zar
3
TABLE OF CONTENTS
Page
Title
i
Abstract
ii
Acknowledge
iii
1
Introduction
1.1 Anatomy and Function of heart
6-8
1.2 Background and Motivation
9-12
1.3 Objectives
13
1.4 Report organization
14
2
Literature Review
14-18
3
Material and Methods
4
5
3.1 Magnetic resonance imaging (MRI) images
19
3.2 Imaging analysis using CMRtools
20-21
3.3 Development of MATLAB algorithm
21-25
Results
4.1 Subject characteristics
26
4.2 Comparison of wall motion analysis in patients to normal subjects
26-32
Discussion
5.1
Wall motion analysis
32-33
5.2
Limitations
33
5.3
Future Directions
34
4
6
Conclusions
References
iv
Appendix A-Figures
Appendix B-MATLAB codes
5
1 Introduction
1.1 Anatomy and Function of heart
Anatomy of the heart
The heart is a specialised muscle that contracts regularly and continuously, pumping
blood to the body and lungs. Heart is located under the ribcage in the centre of the chest between
right and left lungs. The size of the heart can vary depending on a person’s age, size and the
condition your heart. A normal, healthy adult heart is usually the size of a clenched fist although
some disease of the heart could cause the size of the heart to be larger. The heart weighs between
7 and 15 ounces or 200 to 425 grams. In average, the heart beats about 100,000 times and pumps
about 2,000 gallons or 7500 litres of blood, each day.
Fig1: Exterior of heart including coronary arteries and major blood vessels [46]
A double-layered membrane called pericardium surrounds the heart. The outer layer of
the pericardium surrounds the roots of the heart’s major blood vessels and is attached by the
ligaments to spinal column, diaphragm and other parts of your body. Inner layer of the
pericardium is attached to the heart muscle. A coating of fluid separates the two layers of
membrane, allowing heart to move as it beats.
6
Heart has four chambers. Upper chambers are called left and right atria and the lower
chambers are called left and right ventricles. Left and right chambers of the heart are separated by
a wall of muscle called septum. The area of septum that divides the atria is called interatrial
septum and the area that separate the ventricles is called the interventricular septum. In the
normal heart, the left ventricle is the largest and strongest chamber of all as it pushes the
oxygenated blood through the aortic valve and into the body.
Fig2: Cross-section of a heart with its four chambers and four valves that regulate the blood flow
[47]
The heart has four valves that regulate the blood flow. The tricuspid valve regulates blood
flow between right atrium and right ventricle. The pulmonary valve controls blood flow from the
right ventricle into the pulmonary arteries which carry blood to the lungs to pick up oxygen. The
mitral valve allows oxygenated blood from the lungs, passing through the left atrium into the left
ventricle. Lastly, the aortic valve allows the oxygenated blood from the left ventricle into the
aorta, the body’s largest artery, where oxygenated blood is delivered to the rest of the body.
7
Function of the heart
The function of the heart is to pump oxygenated (oxygen-rich) blood to the living cells in
the body and it is vital to the body’s circulatory system. In order to supply oxygenated blood
throughout the body, the heart needs to continuously and regularly beat for a person’s entire
lifespan. For a 70 year old person, the heart would have beaten approximately two to three billion
times and pumped approximately 50 to 60 million gallons of blood through his life span. As the
heart is vital to the circulatory, it is made up of muscles different from skeletal muscles that allow
the heart to constantly beat.
In figure2, the arrow shows the direction and circulation of the blood flow through the
heart. The light blue arrows show that blood enters the right atrium of the heart from the superior
and inferior vena cava. From the right atrium, blood is pumped into the right ventricle. From the
right ventricle, blood is pumped to the lungs through the pulmonary arteries. The red arrows
show the oxygenated blood coming in from the lungs through the pulmonary veins into the
heart’s left atrium. The left ventricle pumps the blood to the rest of your body through the aorta.
In order for the heart to function properly, the blood must flow only in one direction and that is
controlled by the heart’s valves. Both the heart’s ventricle has inlet valve from the atria and outlet
valve leading to the arteries. A normal functioning valve open and close, in exact coordination
with the pumping action of the heart’s atria and ventricles. Each valve has a set of flaps called
leaflets or cusps that seal or open the valves. This allows pumped blood to pass through the
chambers and into the arteries without backing up or flowing backward.
The cardiac cycle is made up of two stages, systole and diastole, as shown in figure 3. The
first stage, systole occurs when the ventricles of the heart are contracting that result in blood
being pumped out to the lungs and the rest of the body. When the thick muscular wall of both
ventricles contract, pressure rises in both ventricles and that causes the mitral and tricuspid valves
to close. Hence, blood is forced up into the aorta and the pulmonary artery. During the time, the
atria relax and the left atrium receives blood from the pulmonary vein and the right atrium from
the vena cava.
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Figure 3: Stages of diastole and systole [48]
The second stage, diastole occurs when the ventricles of the heart are relaxed and not
contracting. During this stage, the atria are filled with blood and pump blood into the ventricles.
The thick muscular walls of both ventricles relax and the pressure in both ventricles falls low
enough for bicuspid valves to open. The atria contracts and blood is forced into the ventricles,
expending them. The blood pressure in the aorta is decreased; hence the semi lunar valves close.
1.2 Background and Motivation
Physiologists have long acknowledged that ventricular geometry is a primary determinant
factor of cardiac function and it plays and important role in the pathophysiological adaptation of
heart to disease. Right ventricular (RV) dysfunction plays an important role in multiple
cardiopulmonary diseases such as pulmonary arterial hypertension, congestive heart failure and
myocardial infarction. Manipulation of loading condition, heart rate, contractility and myocardial
perfusion by the use of physiological and pharmacological procedures also has a major influence
on the right ventricular function and volume [1-5]. The alteration of ventricular volume also
affects the cardiac shape to certain extents. Pathological conditions such as acute myocardial
infarction or prolonged ischemic myocardium are often followed by the ventricular remodelling.
It influences not only the shape of the cardiac and performance but also the patient’s prognosis.
However, the assessment of right ventricular (RV) function remains limited and challenging due
to it’s complexity of geometry and the mechanical interaction with left ventricle (LV).
9
Alteration of right ventricular shape is also common in the patient with left and right
ventricular volume and pressure overloading [6-9]. Therefore, the shape of ventricles is an
important diagnostic and therapeutic index for evaluating a variety of cardiac diseases.
Researchers have studied the relationship of the cardiac shape and the severity of heart disease
for several decades [10-18]. Harvey [19] was the first one to provide an accurate description of
ventricular shape when he mentioned that the left ventricle (LV) becomes ‘narrow, relatively
longer and more drawn together’ during ejection and resumed a more ‘spherical’ configuration
during diastole. However, Rushmer [20] was the first one who characterized the geometric
alterations during cardiac contractions by changes in the major and minor axis diameter. In the
previous studies, the shape analysis is mainly based on two-dimensional tomographic section of
the heart using simple indices or sophistically curvature analysis. More will be described in the
later chapter of ‘review of the theory and previous work’.
Right ventricular(RV) dysfunction is common in pulmonary hypertension (PH),
congenital heart diseases (CHD), coronary artery or vulvular heart disease and in patients with
left sided heart failure (HF). Many studies have been published the prognostic value of RV
function in cardiovascular disease in recent years.
Heart Failure (HF)
RV dysfunction in left ventricular failure could occur in both non ischemic and ischemic
cardiomyopathy. RV dysfunction in HF is the secondary to pulmonary venous hypertension,
intrinsic myocardial involvement, ventricular interdependence and myocardial ischemia.
However, RV dysfunction is more common in non ischemic cardiomyopathy than in ischemic
cardiomyopathy [49]. RV dysfunction is a strong independent predictor of morality in left
ventricular failure. Other indexes of RV function that are associated with worse outcomes in HF
include RV myocardial performance index, and tricuspid annular velocities. It is proven that
tricuspid annular plane systolic excursion is associated with a greater risk of death or heart
transplantation [49].
Exercise capacity is a strong predictor of mortality in HF and it is more closely related to
RV function than LV function. There are only a few studies that address the prognostic
importance of RV diastolic function. The difficulty in studying RV diastolic function explained
the marked load dependence of RV filling indexes. In patients with left ventricular failure, RV
diastolic dysfunction is defined by abnormal filling profiles and it is associated with an increased
risk of nonfatal hospital admissions for HF or unstable angina [49].
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RV Myocardial Infarction (RVMI)
RVMI was first recognized by Saunders in 1930 when he described the triad of
hypotension, elevated jugular veins, and clear lung fields in patients with extensive RV necrosis
and minimal LV involvement. The incidence of RVMI in the context of inferior myocardial
infarction depends on the criteria of the diagnosis. RVMI is associated with an increased risk of
death, cardiogenic shock, ventricular fibrillation. The increased risk is related to the presence of
RV myocardial involvement itself rather than the extent of LV myocardial damage. RV is
resistant to irreversible ischemic injury and myocardial stunning plays an important role in the
pathophysiology of RV dysfunction.
Valvular Heart Disease
RV dysfunction could be seen in both left-sided and right-sided valvular heart disease. Mitral
stenosis often leads to RV dysfunction. RV failure occurs more commonly in patients with severe
mitral stenosis is the cause of mortality. RV dysfunction may be reversed to a significant degree
after mitral valve is repaired or replaced. In chronic mitral regurgitation, significant pulmonary
hypertension (PH) may occur in most the patients and lead to RV dysfunction during exercise at
first and later during at rest. In un-operated patients, semi-normal RVEF at rest is associated with
decreased exercise tolerance, complex arrhythmias, and mortality [49]. Decreased RV systolic
reserve in asymptomatic patients is associated with an increased risk of progression to HF.
RV systolic function is usually maintained in patients with aortic stenosis. However, RV
systolic dysfunction is related to the decreased preoperative cardiac output and a greater
requirement of inotropic support after valvular surgery. Flail tricuspid valve decrease the chance
of survival and a high incidence of HF, atrial fibrillation, and need for valve replacement.
Pulmonary Hypertension (PH)
PH is an increase in blood pressure in pulmonary artery, pulmonary vein, or pulmonary
capillaries, known as lung vasculature. PH could be a severe disease with a markedly decreased
exercise tolerance and heart failure. It can be classified into five different types: arterial, venous,
hypoxic, thromboembolic and miscellaneous. The common symptoms of PH are shortness of
breath, fatigue, non-productive cough, angina pectoris, fainting or syncope, peripheral edema
11
which is the swelling around ankles and feet and sometimes hemoptysis or coughing up blood.
Pulmonary venous hypertension usually presents with shortness of breath while lying flat or
during sleeping while pulmonary arterial hypertension typically does not.
PH could be classified according to WHO’s guidelines [49].
1) WHO Group I- Pulmonary arterial hypertension (PAH)
-Idiopathic (IPAH)
-Familial (FPAH)
- Associated with other diseases (APAH): collagen vascular disease (e.g. scleroderma),
congenital shunts between the systemic and pulmonary circulation, portal hypertension,
HIV infection, drugs, toxins or other diseases or disorders.
-Associated with venous or capillary disease
2) WHO Group II - Pulmonary hypertension associated with left heart disease
- Atrial or ventricular disease
- Valvular disease (e.g. mitral stenosis)
3) WHO Group III - Pulmonary hypertension associated with lung diseases and/or hypoxemia
- Chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD)
- Sleep-disordered breathing, alveolar hypoventilation
- Chronic exposure to high altitude
- Developmental lung abnormalities.
4) WHO Group IV - Pulmonary hypertension due to chronic thrombotic and/or embolic disease
- Pulmonary embolism in the proximal or distal pulmonary arteries.
- Embolization of other matter, such as tumor cells or parasites.
5) WHO Group V - Miscellaneous
Congenital Heart Disease (CHD)
RV failure is common in CHD patients. In CHD patients, the anatomic RV may support the
pulmonary circulation or the systemic circulation. Isolated large ASD results in left-to-right
shunting and volume overload of the RV. Although the RV usually tolerates chronic volume
overload well, long-standing volume overload in the setting of an ASD is related to increased
mortality and morbidity.
Tetrology of Fallot (TOF) is a severe congenital heart defect that requires surgical
procedure to repair early in infancy. It basically involves four anatomical abnormalities, although
12
only three of the conditions are normally present. It is also the most common form of heart defect
which is the main cause of blue baby syndrome in infants. TOF represents about 55-70% of the
heart defects [49]. As the name suggests, the four common conditions of TOF are pulmonary
stenosis, overriding aorta, ventricular septal defect (VSD) and right ventricular hypertrophy.
Pulmonary stenosis is a narrowing of the right ventricular outflow tract and can occur at the
pulmonary valve or just below the valve. It is mainly caused by overgrowth of the heart muscle
walls and also the main cause of the malformations. The overriding aorta is a condition in which
the aortic valve with biventricular connection that is situated above the ventricular septal defect
and connected to both right and left ventricles. The degree of override (quite variable with 595%) is the degree to which the aorta is attached to the right ventricle.
RV outflow obstruction could occur in a number of congenital abnormalities such as
pulmonary valve stenosis, double-chambered RV, infundibular hypertrophy and dynamic
obstruction of the RV outflow tract. The RV usually adapts well to pulmonary valve stenosis even
when the condition is severe. In patients with moderate to severe pulmonary valve stenosis,
symptoms are not common during childhood and adolescence. In adults, symptoms of fatigue and
dyspnea usually reflect the inability to increase cardiac output with exercise. In the long run,
untreated severe obstruction will lead to RV failure and tricuspid regurgitation.
1.3 Objectives
The human heart is one of the most complex biological systems. It remains a challenge to
understand each component of the heart and its system. This understanding will have huge
benefits that would result in health care and medical practice.
The overall objective of this project is to develop an algorithm to report the contour map
and to automatically compute displacement of the right ventricular regional wall motion from
magnetic resonance imaging (MRI) images.
Project Objective
The objective of this project is to develop a quantitative method to analyse the right ventricular
motion in human. In this project a MATLAB algorithm was developed to report the contour map
from magnetic resonance imaging (MRI) images and to compute the displacement of right
ventricle during cardiac cycle.
13
1.4 Report organization
In the introduction chapter of this report, anatomy and function of heart was described to
familiarize the readers, followed by the background and motivations of the study. The major
common diseases of right ventricle were also summarized to emphasize the importance of RV
wall motion analysis. The objective of the study was also mentioned.
The second chapter of this report contained the review of literatures. I have cited several
independent articles and papers that are relevant to the area of ventricular analysis and, the
shortcomings and advantages of the methods available. Most of the studies available are in the
area of left ventricular (LV) analysis; hence it shows that limited studies have been done on RV
wall motion analysis.
The third chapter reported the materials and the methods used in this study. It is again
categorize into 3 parts. The first part described the Magnetic resonance imaging (MRI) images.
The second part descried imaging analysis using CMRtools software. The latest part described
the main development of MATLAB algorithm.
The fourth chapter reported the results obtained by applying the method in third chapter.
The details of subjects/ volunteers recruited were also described. Comparison of wall motion
analysis in patients to normal subjects was done in this chapter.
In the fifth chapter, RV wall motion was analysed based on the results obtained and the
limitations of the methods and the recommended future works. In the last chapter, the reflection
was done on the whole of the study and that I was able to meet the objective of the study and that
successfully developed the quantitative method to analyse the RV wall motion.
14
2 Literature Review
Researchers have studied the relationship of the cardiac shape and the severity of heart disease
for several decades. Rushmer [20] was the first one who characterized the geometric alterations
during cardiac contractions by changes in the major and minor axis diameter. Cardiac contraction
is associated with a greater decrease in minor axis diameter, so that the ventricle becomes
elliptical during systole. On the other hand, the major dimensional change during diastolic filling
is an increase in minor axis, which tends to make the ventricle more spherical. Many
investigators have since described changes in major and minor axis dements and of the ratio of
major to minor axis during evolving heart failure due to either coronary artery disease or
idiopathic dilated cardiomyopathy [27-30]. However, the use of simple dimensional changes is
limited because they reflect only linear alterations in the two axes and assume that no regional
wall motion abnormalities are involved. Besides dimensional changes, other methods have also
been explored to analyze regional ventricular shape changes. Eccentricity is a common index that
compares the actual shape of the heart with an elliptical mode [31]. Gibson and brown [32] have
used a shape index that relates the observed area to its perimeters. This index is based on a
circular model and has a maximum value of 1 when the area is completely circular and a
minimum of zero when there is cavity obliteration. These two indexes are based on idealized
geometric shapes, which limit their applications. Kass et al [31] used Fourier analysis to
transform the observed shape into individual series components. Although this methodology
provides a precise description of the shape of the ventricle, the physiological significance of the
series components of the Fourier transformation is uncertain.
The shape of the ventricle, however, is determined primarily by the curvature of its wall.
Hence, curvature analysis may be a practical method in the study of ventricular shapes.
Previously, Mancini et al [33] described the use of quantitative regional curvature analysis in
contrast left ventriculograms. This method has various advantages. It is devoid of defined
reference and coordinate systems free of idealized geometric assumptions and not invalidated by
wall motion abnormalities. Several groups have applied this method in conjunction with contrast
ventriculography to assess regional wall motion abnormalities in humans [34-36]. The use of a
single-plane contrast ventriculogram, however, has some drawbacks: first, the outline of this
single projection is not anatomically continuous of this single projection but is a combination of
several overlapping boundaries. Secondly, the margins of the cavity may be formed by papillary
muscles rather than the free wall of the ventricle itself, and lastly, the invasive nature of the
15
procedure limits serial study. Therefore, Chan et al [37] studied the alterations in ventricular
shape during normal cardiac contractions in the dog by quantitative regional curvature analysis
on ventricular outlines obtained by echocardiography and compared them with results from
traditional methods of shape analysis. Later on, several studies have shown that threedimensional echo provide a better description of cardiac pathology and accuracy in quantification
of ventricular volume and function than two-dimensional images. Reng et al [38] has described a
new approach, a 3 dimensional volumetric curvature analysis (3 DVCA) that yields the variety of
shape descriptors on regional and global left ventricular shape from 3D echocardiographic
images.
Figure 23: 3D echocardiography image [38]
Marisa [41] did echo studies of 178 patients. In this study, three types of LV shape
abnormalities were identified: type 1 being true aneurysm, type 2 being nonaneurysmal lesions
defined as intermediate cardiomyopathy, and type 3 being ischemic dilated cardiomyopathy.
Myocardial infarction could result in a spectrum of left ventricular (LV) shape abnormalities.
Surgical ventricular restoration (SVR) can be applied to any, but there were no data that relate its
effectiveness to LV shape. Moreover, there is no consensus on the benefit of SVR in patients
with a markedly dilated ventricle, without clear demarcation between scarred and normal tissue.
16
Figure 24: Ventricular abnormalities due to Hypertrophy [49]
This study described postmyocaridal infarction shape abnormalities and cardiac function,
clinical status, and survival in patients undergoing SVR. The results showed that SVR induced
significant improvement in cardiac and clinical status in all patients, regardless of LV shape
types. Although not significant, mortality was higher in types 2 and 3. Therefore, ischemic
dilated cardiomyopathy and not just the true aneurysm can be successfully treated with SVR.
Shape classification may be useful to improve patient selection and compare results from
different institutions that are otherwise impossible to compare.
Regional left ventricular (LV) curvature analysis is a useful tool to assess the
pathophysiological changes in LV shape which occur in different heart diseases. The study was
done using curvature-motion method (CM) by Barletta G [40]. As LV shape changes follow
regular trajectories, they used the curvature extrema and the normalized curvature variations as
the features for identifying the movement of the borders during the cardiac cycle (curvature
motion method: CM). The regional curvature was calculated using a windowed Fourier series
approximation of contours, in which the number of harmonics and filter-window were locally
chosen in order to minimize the reconstruction errors and to maximize the smoothness of the
curve. Analysis programs were tested on a series of ventricle-shaped contours, software
generated. Left ventricular diastolic and systolic outlines obtained from RAO 30 degree LV
angiography in 24 patients with aortic insufficiency and in 16 subjects without heart disease were
analyzed. Left ventricular curvature and regional wall motion were calculated in each subject. In
17
respect to normal subjects, LV shape in aortic regurgitation definitely appears asymmetric
because of the elongation of the anterior hemiperimeters and the prevailing expansion of the
apical and anterolateral regions. These alterations in cavity geometry correlate to the decrease in
pump function. According to these results, wall motion using the CM showed a greater extension
of LV a synergy, while usual methods at the centreline or the radial one indicate a greater damage
of the apical regions. Hence, the CM methods seem to be promising for wall motion analysis.
The local curvature function could be defined as the curvature change around the LV wall
circumference. In another study by M. Halmann [39], the local instantaneous curvature function
is used to quantify regional left-ventricular performance throughout the cardiac cycle. Left
ventriculography images, taken in the right anterior oblique (RAO) view from nine patients with
normal ventricular contraction and eight patients with anterior hypokinesis (AHK) were used.
The local curvature around the circumference of LV is calculated for each heart throughout the
ejection period. The dynamic increase in the curvature of the apex defined as apical sharpening is
a typical feature of LV contraction. Apical sharpening from end-diastole to end-systole is closely
related to the degree of hypokinesis. Normal hearts show larger apical sharpening (128± 57%,
SD) than do AHK hearts (46± 13%, p=0.002). The ratio between apical and anterior curvatures at
ES has been found to be 7±3.5 for normal hearts and 2.3 ± 0.6 for AHK hearts (p=0.003). Linear
regression between the ventricular volume and apical curvature yields a significant relationship
for the normal hearts (r=0.82 ± 0.06, average p=0.07), but not for the AHK hearts (r=0.72 ± 0.2,
average p=0.34). Therefore, the information inherent in the local curvature of the LV and its
dynamic changes throughout the cardiac cycle may be used to distinguish between normal and
anterior hypokinetic hearts (NHK).
In the latest study, Francesco [42] tested the feasibility of 3D analysis of regional LV
endocardial curvature from CMR images in a relatively large number of patients with different
patterns of wall motion. 38 patients with 14 normal LV function (NL), 6 with idiopathic dilated
cardiomyopathy (IDC) and 18 patients with wall motion abnormalities secondary to ischemic
heart disease (IHD). Steady-state free precession images were obtained in short-axis views from
base to apex as well as 2-, 3- and 4 chamber views. After the endocardial boundaries were
initialized in the long axis views, LV endocardial surface was semi-automatically reconstructed
throughout the cardiac cycle (LV analysis MR, TomTec). Custom software was used to calculate
for each point on the surface the maximum curvature and the curvature in the perpendicular
direction and local surface curvedness (C) was calculated as the root mean square. C values were
averaged using standard 17-segment model and compared between groups of segments: NL
(N=401), IDC (N=98) and IHD (N=153) using one-way ANOVA. In all normal segments, C
18
gradually increased during systole and then decreased during diastole. While both maximum and
minimum values of C were comparable in the 6 basal and 6 mid-ventricular segments, they were
significantly higher in the 4 apical segments and highest in the apical cap. Additionally, percent
change in C was higher mid and apical compared to basal segments (P<0.05). At all LV levels, C
values in IDC segments were lower (p<0.05) than in NL and IHD segments, which were similar.
In contrast, percent change in C was significantly lower in both IHD and IDC segments compared
to NL, Figure 11.
Figure 11: Maffessanti et al. Journal of Cardiovascular Magnetic Resonance 2010 12 (Suppl
1):P236 doi: 10.1186/1532-429X-12-S1-P236
During this literature research, it is realized that quantitative methods to study regional
wall motion of right ventricles are limited compared to studies of curvature analysis of left
ventricles. In the study done by Miura [43], right ventricular function was assessed by regional
wall motion analysis and by global function in 62 patients after repair for Tetralogy of Fallot
(TOF). Its relation to surgical procedures, with special attention to right ventriculotomy, was
investigated. The results from this study indicated that transpulmonary-transatrial repair for TOF
provided better postoperative global right ventricular function and its reserve, with less impaired
regional wall motion, than did the transventricular repair.
Eyll [44] used a procedure to evaluate the right ventricular function parameters during
cardiac catheterization. The procedure only requires a right-sided catheterization. It can also be
repeated in outpatients for serial investigations. When compared with similar analyses with
radionuclide techniques, this approach offers the advantage of a superior geometric resolution
and the benefit of simultaneous high-fidelity pressure recording.
19
One of the very few quantitative studies of RV is done by Julia [45] by analyzing the
angiographic contours of the RV in three views to quantify RV wall motion based on contrast
ventriculography in patients with ARVD/C and to specify the severity and location of wall
motion abnormalities, as compared with normal subjects.
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3 Material and Methods
3.1 Magnetic resonance imaging (MRI) images
MRI provides information that differs from the other imaging modalities such as
Echocardiogram and Computed tomography (CT). Its major technological advantage is that it is
able to characterize and discriminate among tissues using their physical and biochemical
properties such as water, iron, fat, extra vascular blood and its breakdown products. In related to
cardiac diagnosis, MRI has the potential of replacing at least four other cardiac tests:
Echocardiogram, Multi gated acquisition scan (MUGA), Thallium scan, and Diagnostic cardiac
catheterization [21-22]. MRI produces sectional images of equivalent resolution in any projection
without moving the patient. The ability to obtain images in multiple planes adds to its versatility
and diagnostic utility and offers special advantages for radiation and other surgical treatment
planning. Moreover, MRI does not involve exposing of the patient to ionizing potentially harmful
radiation unlike other most of the non-invasive cardiac imaging tests.
In regards to this project, using MRI images has several advantages. 1) The images
generated by MRI are remarkably complete, detailed and precise more than other cardiac imaging
tests. 2) There is a good compromise between spatial resolution and temporal resolution of the
images. 3) There is an excellent signal contrast between heart muscle and blood. That makes it
easier when we trace the shape of the left ventricle in the CMR tools. 4) The images yield
accurate definition of endocardial and epicedial borders. 5) There is accurate quantization of LV
volumes and mass without the need for geometric assumptions. 6) MRI produces sectional
images of equivalent resolution in any projection without moving the patient. 7) the most
important aspect of using MRI images in this project is the ability to obtain images in multiple
planes. This makes it possible to export the coordinates of the multiple planes from CMR. In this
project MRI images of 9 subjects were used.
3.2 Imaging analysis using CMRtools
CMRtools is a software package for the visualization and analysis of Cardiovascular
Magnetic Resonance Images (MRI). It has other related software packages called LVtools,
3Dtools and Perfusion Tools. CMRtools is designed for the clinical research community and
contains efficient tools that are dedicated for cardiovascular research.
21
In its simplest form, CMR tools could be used as a standalone DICOM image viewer that
provides rapid, versatile image browsing and Regional of Interest (ROI) analysis. When CMR is
used in conjunction with other different plug-in packages of CMRtools, it provides advanced
CMR quantification and modelling capabilities. The plug-ins provide and integrated and easy to
follow analysis workflow that can significantly enhance the productivity and research potential.
CMRtools is the baseline software package provided by CVIS. It provides everything that is
needed to import DICOM images from a CD/DVD or a network drive. Its multi-format viewing
window permits viewing, annotation, ROI analysis, and access to specialist plug-ins. The
software uses a format for saving CMR sessions so that the intermediate analysis results could be
saved, retrieved or continued at any time.
This also provides an auditing trail for future
references and to compare different quantification methods. CMRtools is designed to run on
standard PCs including laptops with Microsoft Windows. One of the unique features of
CMRtools is its intuitive and efficient user interface and user-friendly analysis workflow. [23]
In this project, Ventricular tools and ROI analysis that analyse the left and right ventricles
are used. MRI image from set of the subjects was chosen and loaded into CMRtools as shown in
Figure 4. Four chambers (4c) view from the set of images is selected for processing in this
project. There are a total of 25 frames with diastolic and systolic phases for each subject.
Fig 4: MRI image-set in CMRtools
Right ventricle (RV) was identified from the frames and the contour outline of the RV
was traced using the ‘draw’ tool for the first frame. The contour outline was then smoothened and
22
edited to fit the shape of the right ventricle wall. The outline of the first frame are then copied and
pasted onto the rest of 24 frames. They were then adjusted accordingly to fit the shape of the
systolic and diastolic phases (Fig 5).
Fig 5: Outline of the contour-shape of the right ventricle (RV)
After completion, the ROI coordinates of each of the 25 frames are exported (Fig 6) and
the coordinates are saved as text files (Fig 7) which were later used to process in Matlab software.
Fig 6: Exporting ROI contour point coordinates
23
Fig 7: Contour coordinates for each frame are saved as text file to be later imported into Matlab.
3.3 Development of MATLAB algorithm
MATLAB stands for Matrix Laboratory and it is a numerical computing environment and
fourth generation programming language. MATLAB is developed by the MathWorks, and it
allows matrix manipulations, plotting of functions and data, implementation of algorithms,
creation of user interfaces, and interfacing with programs written in other languages such as C
and C++ [24-26]. In this project, MATLAB was used to create an algorithm that is able to
convert CMRtools coordinates output into overlapping contour shapes, and displacement of the
systolic and diastolic phases.
First the coordinates from CMRtools were extracted and plotted. The following is an extract of
the algorithm to achieve the first frame plot.
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\Subject 1\1.txt','%f
%f %f')
% Creating plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0 0 0],...
'DisplayName','C 1');
The nex part is to use the Hold function to overlap all 25frames of the subject.
box('on');
hold('all');
24
And the process was repeated for all 25frames to achieve the following contour plot (Figure 8).
Figure 8: Contour plots of RV
Plotting of RV wall displacement
There are some limitations when developing RV wall’s movement from frame to frame (from
diastolic phase to systolic phase and back to diastolic phase). This limitation is mainly due to inaccuracy and repeatability issue when tracing the RV wall outline in CMR tools since the contour
map tracing is done manually. This in turn causes the starting point of the contour map to be
inconsistent from frame to frame. To correct this error as much as possible, an additional
calibration module is added in the algorithm (Figure 9).
25
Figure 9: Contour map of RV wall after calibration
Below is an extract.
%Calibration module that would determine starting points
k1 = 30
k2 = 20
k3 = 12
k4 = 24
k5 = 15
k6 = 10
k7 = 16
k8 = 19
k9 = 21
k10= 19
% Extraction and ploting graph
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\Subject 1\1.txt','%f
%f %f')
plot(x,y,'Parent',axes1,'Marker','.','LineWidth',1,'DisplayName','C 1',...
'Color',[0 0 0]);
x1 = x'
y1 = y'
plot(x1(k1),y1(k1),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
0 0]);
% Calulation module that take reference from calibration module.
26
done1 = (sqrt((x1(k1)-x24(k24))^2 + (y1(k1)-y24(k24))^2))/34.84;
xi=[1 2 3 4 5 6 7 8 9 10];
x=xi'
% Create axes
axes2 = axes('Parent',figure1,'Position',[0.5929 0.11 0.1788 0.815],...
'LineWidth',1);
yi=[done1 done2 done3 done4 done5 done6 done7 done8 done9 done10];
y=yi'
% Create axes
axes3 = axes('Parent',figure1,'Position',[0.8165 0.11 0.1749 0.815]);
xlim([1 10]);
box('on');
hold('all');
% Create plot
plot(x,y,'Parent',axes3,'Marker','.','LineWidth',2);
% Create title
title({Contours outline of LV'});
The algorithm is able to report the contour outline of LV walls and the displacement from
Diastolic to Systolic phases, Figure 10.
Figure 10: Contour outline of LV wall and Displacement
27
4 Results
4.1 Subject characteristics
9 subjects who underwent Magnetic Resonance Imaging (MRI) scans were recruited for
this study. 4 subjects are healthy normal volunteers without any major medical problem while the
other 5 are patients with right ventricular (RV) dysfunction. 4 subjects consist of 1female and
3males, with ages ranging from 16 to 29 years old and 5 patients with RV dysfunction consist of
2females and 3males, with ages ranging from 14 to 60 years old. At the end of this study, it is
able to differentiate the RV dysfunctional patients from normal subjects.
4.2 Comparison of wall motion analysis in patients to normal subjects
In this study, I’ve studied 9 subjects: 4 with normal right ventricle function (NRV) and 5
with abnormalities of right ventricle (PRV).
- Right ventricle contour maps of 25frames for each subject were automatically re-constructed in
MATLAB (figures 12-16).
- The contour maps were then segmented into 9 regions using a calibration algorithm in
MATLAB. Segment 1 and 9: Basal region, Segment 2 and 8: Basal to Mid region, Segment 3 and
7: Mid region, and Segmen4, 5 and 6: Apex region (Figure 17).
-Displacement values of each region at peak systolic stage were calculated using MATLAB
algorithm (figure 18-22).
Figure 12: Contour map of Right Ventricle of Subject 1 and subject 2
28
Figure 13: Contour map of Right Ventricle of Subject 3 and subject 4
Figure 14: Contour map of Right Ventricle of Patient 1 and Patient 2
Figure 15: Contour map of Right Ventricle of Patient 3 and Patient 4
29
Figure 16: Contour map of Right Ventricle of Patient 5
Subject 1
Subject 2
Subject 3
Subject 4
Patient 1
Patient 2
Patient 3
Patient 4
Patient 5
Contour shape
Elongated, Cresentic and trapezoidal shape
Elongated, Cresentic and trapezoidal shape
Elongated, Cresentic and trapezoidal shape
Elongated, Cresentic and trapezoidal shape
Larger cross-sectional area and highly dilated
Larger cross-sectional area and highly dilated
Larger cross-sectional area and highly dilated
Larger cross-sectional area and highly dilated
Larger cross-sectional area and highly dilated
Figure 17: Nine segments of Right ventricle
30
Figure 18: Displacement values (in mm) of Subject 1 and 2
Figure 19: Displacement values (in mm) of Subject 3 and 4
31
Figure 20: Displacement values (in mm) of Patient 1 and 2
Figure 21: Displacement values (in mm) of Patient 3 and 4
32
Figure 22: Displacement values (in mm) of Patient 5
Table 1. Regional displacement in normal subjects (NRV) and patients with right ventricular
dysfunction (PRV)
Normal Subjects' RV Regional displacement at Systolic
LV Region
Basal
Basal
Basal-Mid
LV Segment
Segment 1
Segment 9
Segment 2
NRV1
0.38
0.38
0.33
NRV2
0.4424
0.4877
0.3513
NRV3
0.324
0.3589
0.2245
NRV4
0.3664
0.2751
0.3431
Mean
0.3782
0.37543
0.31223
Std
0.049
0.08749
0.05914
Mean of Mean
0.38
0.29
Mean of Std
0.07
0.08
Patients' RV Regional displacement at Systolic
LV Region
Basal
Basal
Basal-Mid
LV Segment
Segment 1
Segment 9
Segment 2
PRV1
0.2269
0.26
0.143
PRV2
0.1768
0.1816
0.176
PRV3
0.266
0.2109
0.2106
PRV4
0.2572
0.2879
0.1293
PRV5
0.1391
0.139
0.1404
Mean
0.2132
0.21588
0.15986
Std
0.05415
0.05967
0.03329
Mean of Mean
0.21
0.19
Mean of Std
0.06
0.06
33
Mid-Basal
Segment 8
0.3
0.4034
0.2366
0.17
0.2775
0.09931
Mid-Basal
Segment 8
0.33
0.1865
0.1812
0.2695
0.126
0.21864
0.08063
Normal Subjects' RV Regional displacement at Systolic
LV Region
Mid
Mid
Apex
LV Segment
Segment 3
Segment 7
Segment 4
NLV1
0.25
0.18
0.171
NLV2
0.2102
0.283
0.06899
NLV3
0.1145
0.1261
0.07988
NLV4
0.2473
0.08917
0.1318
Mean
0.2055
0.16957
0.11292
Std
0.06333
0.08432
0.04744
Mean of Mean
0.19
0.11
Mean of Std
0.07
0.05
Patients' RV Regional displacement at Systolic
LV Region
Mid
Mid
Apex
LV Segment
Segment 3
Segment 7
Segment 4
PLV1
0.081
0.21
0.14
PLV2
0.2512
0.164
0.2231
PLV3
0.1678
0.08397
0.1527
PLV4
0.1115
0.1348
0.2451
PLV5
0.09668
0.1145
0.2063
Mean
0.14164
0.14145
0.19344
Std
0.06945
0.04817
0.04536
Mean of Mean
0.14
0.17
Mean of Std
0.06
0.05
Apex
Segment 5
0.153
0.07804
0.1559
0.06557
0.11313
0.048
Apex
Segment 6
0.12
0.163
0.1285
0.0568
0.11708
0.04428
Apex
Segment 5
0.09
0.2591
0.1774
0.2094
0.1675
0.18068
0.06201
Apex
Segment 6
0.135
0.1851
0.1201
0.07874
0.2199
0.14777
0.05543
From Figures 12-13, the right ventricular shape was elongated, cresentic and trapezoidal
in the normal subjects. However, for the patients, cross-sectional area was significantly larger and
they are highly dilated or round compared to normal subjects (Figures 14-16).
From Figures 18-19, the displacements increased during systole and decreased during
diastole in normal subjects. There are similar trends of displacement in patients with right
ventricular dysfunction (Figure 20-22). However, maximal displacement was much lower in
patients compared to normal subjects (Table 1), in particular at basal regions.
34
5 Discussions
5.1 Wall motion analysis
From the results obtained, all 4 normal subjects have a typical right ventricle shape of
elongated, cresentic and trapezoidal. However for the patients, cross-sectional area is
significantly larger and they are highly dilated or round compared to normal subjects. This could
be due to abnormal pressure loading or chronic volume overloading of the ventricles due to long
standing mitral and aortic regurgitation, or hypertrophy in patients with ischemic heart diseases.
Displacement(mm)
Displacement of Right Ventricular Segments
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
SEG1
SEG2
SEG3
SEG4
Basal
Basal-Mid
Mid
Apex
SEG5
SEG6
SEG7
SEG8
SEG9
Apex
Apex
Mid
Mid-Basal
Basal
NRV1
NRV2
NRV3
NRV4
PRV1
PRV2
PRV3
PRV4
PRV5
RV segments
Figure 23: Displacement of right ventricular segments
Comparing the results of displacement for the 9segments for all 9 subjects (figure 23),
displacement value at peak systolic phase were significantly higher in the 2 Mid to Basal
segments and the highest in the 2 Basal segments.
Comparing the results of displacement values between Normal subjects (NRV) and
Patients (PRV), displacement value of all NRV at Basal segments are higher (>0.3mm) than PRV
(< 0.25mm). It is also observed that displacement graphs for PRV have more variations/multiple
high peaks compared to NRV. The low displacement values from diastolic phase to systolic
phase for all the patients suggest RV dysfunction.
35
5.2 Limitations
Contour outline tracing of right ventricles in CMR tools could produce variations in the
starting point of the coordinates as the outline tracing would be highly dependent on individuals.
Therefore, a more systematic or automatic approach should be developed for RV shape outline
tracing. This would produce a more repeatable and reproducible coordinates from CMR. In this
study, the sample size is limited as only 9subjects: 4 normal and 5 patients were studied. This
small sample size could affect the statistical analysis. Furthermore, the current model developed
focus on 2D wall motion analysis. 3D wall motion analysis would be more promising with the
advance of medical technology.
5.3
Future Directions
It is recommended that the method developed be applied to a larger group of subjects to
validate the results obtained in this study. Therefore, the reference/baseline values for normal and
patient with RV dysfunction will be developed. The reference/baseline value could be applied in
clinical pathway to facilitate the patient diagnostic and management at the stage of the heart
failure. Although 2D analysis is useful for research purpose and time efficient, it is not as
comprehensive as 3D analysis. Therefore, 3D wall motion analysis with more regional
segmentation is also expected in order to overcome the limitations of 2D wall motion analysis.
36
6 Conclusions
The importance of right ventricular (RV) dysfunction is increasingly recognized in
multiple cardiopulmonary diseases such as pulmonary arterial hypertension, congestive heart
failure and myocardial infarction. However, the assessment of RV function remains limited and
challenging due to it’s complexity of geometry and the mechanical interaction with left ventricle
(LV). In this study, I have developed an algorithm /approach to analyze the right ventricular
motion automatically. There were distinctive differences in wall motion in patients with RV
dysfunction compared to normal subjects. This new approach may facilitate the heart disease
diagnostic and management. It was also useful to evaluate the effectiveness of therapeutic
intervention in patient with severe right ventricular failure.
37
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43
Appendix A
Displacements
44
Contour Maps
Contour map of Right Ventricle of Subject 1 and subject 2
45
Contour map of Right Ventricle of Subject 3 and subject 4
Contour map of Right Ventricle of Patient 1 and Patient 2
Contour map of Right Ventricle of Patient 3 and Patient 4
46
Contour map of Right Ventricle of Patient 5
Appendix B
% Create figure
figure1 = figure;
% Create axes
axes1 = axes('Parent',figure1,'Position',[0.01422 0.03721 0.5505 0.9202]);
% Uncomment the following line to preserve the X-limits of the axes
% xlim([40 120]);
% Uncomment the following line to preserve the Y-limits of the axes
% ylim([70 130]);
box('on');
hold('all');
% Segmentation 1
k1 = 37
k2 = 37
k3 = 37
k4 = 37
k5 = 37
k6 = 37
k7 = 37
k8 = 37
k9 = 37
k10= 37
k11= 37
k12= 37
k13= 37
k14= 37
k15= 37
k16= 37
k17= 37
k18= 37
k19= 37
k20= 37
k21= 37
k22= 37
k23= 37
k24= 37
47
k25= 37
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\1.txt','%f %f %f')
plot(x,y,'Parent',axes1,'Marker','.','LineWidth',1,'DisplayName','C 1',...
'Color',[0 0 0]);
x1 = x'
y1 = y'
plot(x1(k1),y1(k1),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
0 0]);
one1 = (sqrt((x1(k1)-x1(k1))^2 + (y1(k1)-y1(k1))^2))/34.84;
done1 = (sqrt((x1(k1)-x1(k1))^2 + (y1(k1)-y1(k1))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\2.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.2471 0.2471 0.2471],...
'DisplayName','C 2');
x2 = x'
y2 = y'
plot(x2(k2),y2(k2),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
.2471 0.2471 0.2471]);
one2 = (sqrt((x1(k1)-x2(k2))^2 + (y1(k1)-y2(k2))^2))/34.84;
done2 = (sqrt((x1(k1)-x2(k2))^2 + (y1(k1)-y2(k2))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG
(HUANG WUXIN)\3.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'Color',[0 1 0],'DisplayName','C 3');
x3 = x'
y3 = y'
48
WU
XIN
CHARLES
plot(x3(k3),y3(k3),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
1 0]);
one3 = (sqrt((x2(k2)-x3(k3))^2 + (y2(k2)-y3(k3))^2))/34.84;
done3 = (sqrt((x1(k1)-x3(k3))^2 + (y1(k1)-y3(k3))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\4.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0 0.749 0.749],...
'DisplayName','C 4');
XIN
CHARLES
x4 = x'
y4 = y'
plot(x4(k4),y4(k4),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
0.749 0.749]);
one4 = (sqrt((x3(k3)-x4(k4))^2 + (y3(k3)-y4(k4))^2))/34.84;
done4 = (sqrt((x1(k1)-x4(k4))^2 + (y1(k1)-y4(k4))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG
(HUANG WUXIN)\5.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'Color',[1 0 0],'DisplayName','C 5');
WU
XIN
CHARLES
x5 = x'
y5 = y'
plot(x5(k5),y5(k5),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[1
0 0]);
one5 = (sqrt((x4(k4)-x5(k5))^2 + (y4(k4)-y5(k5))^2))/34.84;
done5 = (sqrt((x1(k1)-x5(k5))^2 + (y1(k1)-y5(k5))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09
(HUANG WUXIN)\6.txt','%f %f %f')
2nd
49
SEM\FYP\Normal\NG
WU
XIN
CHARLES
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0 0 1],'DisplayName','C 6');
x6 = x'
y6 = y'
plot(x6(k6),y6(k6),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
0 1]);
one6 = (sqrt((x5(k5)-x6(k6))^2 + (y5(k5)-y6(k6))^2))/34.84;
done6 = (sqrt((x1(k1)-x6(k6))^2 + (y1(k1)-y6(k6))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\7.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.749 0 0.749],...
'DisplayName','C 7')
x7 = x'
y7 = y'
XIN
CHARLES
plot(x7(k7),y7(k7),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
.749 0 0.749]);
one7 = (sqrt((x6(k6)-x7(k7))^2 + (y6(k6)-y7(k7))^2))/34.84;
done7 = (sqrt((x1(k1)-x7(k7))^2 + (y1(k1)-y7(k7))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\8.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.749 0.749 0],...
'DisplayName','C 8');
x8 = x'
y8 = y'
XIN
CHARLES
plot(x8(k8),y8(k8),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
.749 0.749 0]);
one8 = (sqrt((x7(k7)-x8(k8))^2 + (y7(k7)-y8(k8))^2))/34.84;
50
done8 = (sqrt((x1(k1)-x8(k8))^2 + (y1(k1)-y8(k8))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG
(HUANG WUXIN)\9.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.6 0.2 0],...
'DisplayName','C 9');
WU
XIN
CHARLES
x9 = x'
y9 = y'
plot(x9(k9),y9(k9),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color',[0
.6 0.2 0]);
one9 = (sqrt((x8(k8)-x9(k9))^2 + (y8(k8)-y9(k9))^2))/34.84;
done9 = (sqrt((x1(k1)-x9(k9))^2 + (y1(k1)-y9(k9))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\10.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'Marker','o','Color',[0.3137 0.3137 0.3137],...
'DisplayName','C 10');
x10 = x'
y10 = y'
plot(x10(k10),y10(k10),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.3137 0.3137 0.3137]);
one10 = (sqrt((x9(k9)-x10(k10))^2 + (y9(k9)-y10(k10))^2))/34.84;
done10 = (sqrt((x1(k1)-x10(k10))^2 + (y1(k1)-y10(k10))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\11.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.2471 0.2471 0.2471],...
'DisplayName','C 11');
x11 = x'
y11 = y'
51
plot(x11(k11),y11(k11),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.2471 0.2471 0.2471]);
one11 = (sqrt((x9(k9)-x11(k11))^2 + (y9(k9)-y11(k11))^2))/34.84;
done11 = (sqrt((x1(k1)-x11(k11))^2 + (y1(k1)-y11(k11))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\12.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.2471 0.2471 0.2471],...
'DisplayName','C 12');
x12 = x'
y12 = y'
plot(x12(k12),y12(k12),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.2471 0.2471 0.2471]);
one12 = (sqrt((x11(k11)-x12(k12))^2 + (y11(k11)-y12(k12))^2))/34.84;
done12 = (sqrt((x1(k1)-x12(k12))^2 + (y1(k1)-y12(k12))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\13.txt','%f %f %f')
plot(x,y,'Parent',axes1,'Marker','.','LineWidth',1,'DisplayName','C 13',...
'Color',[0 0 0]);
x13 = x'
y13 = y'
plot(x13(k13),y13(k13),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0 0 0]);
one13 = (sqrt((x12(k12)-x13(k13))^2 + (y12(k12)-y13(k13))^2))/34.84;
done13 = (sqrt((x1(k1)-x13(k13))^2 + (y1(k1)-y13(k13))^2))/34.84;
52
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\14.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.2471 0.2471 0.2471],...
'DisplayName','C 14');
x14 = x'
y14 = y'
plot(x14(k14),y14(k14),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.2471 0.2471 0.2471]);
one14 = (sqrt((x13(k13)-x14(k14))^2 + (y13(k13)-y14(k14))^2))/34.84;
done14 = (sqrt((x1(k1)-x14(k14))^2 + (y1(k1)-y14(k14))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\15.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'Color',[0 1 0],'DisplayName','C 15');
XIN
CHARLES
x15 = x'
y15 = y'
plot(x15(k15),y15(k15),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0 1 0]);
one15 = (sqrt((x14(k14)-x15(k15))^2 + (y14(k14)-y15(k15))^2))/34.84;
done15 = (sqrt((x1(k1)-x15(k15))^2 + (y1(k1)-y15(k15))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\16.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0 0.749 0.749],...
'DisplayName','C 16');
x16 = x'
y16 = y'
53
XIN
CHARLES
plot(x16(k16),y16(k16),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0 0.749 0.749]);
one16 = (sqrt((x15(k15)-x16(k16))^2 + (y15(k15)-y16(k16))^2))/34.84;
done16 = (sqrt((x1(k1)-x16(k16))^2 + (y1(k1)-y16(k16))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\17.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'Color',[1 0 0],'DisplayName','C 17');
XIN
CHARLES
x17 = x'
y17 = y'
plot(x17(k17),y17(k17),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[1 0 0]);
one17 = (sqrt((x16(k16)-x17(k17))^2 + (y16(k16)-y17(k17))^2))/34.84;
done17 = (sqrt((x1(k1)-x17(k17))^2 + (y1(k1)-y17(k17))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\18.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0 0 1],'DisplayName','C 18');
x18 = x'
y18 = y'
plot(x18(k18),y18(k18),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0 0 1]);
one18 = (sqrt((x17(k17)-x18(k18))^2 + (y17(k17)-y18(k18))^2))/34.84;
done18 = (sqrt((x1(k1)-x18(k18))^2 + (y1(k1)-y18(k18))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\19.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.749 0 0.749],...
54
XIN
CHARLES
'DisplayName','C 19')
x19 = x'
y19 = y'
plot(x19(k19),y19(k19),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.749 0 0.749]);
one19 = (sqrt((x18(k18)-x19(k19))^2 + (y18(k18)-y19(k19))^2))/34.84;
done19 = (sqrt((x1(k1)-x19(k19))^2 + (y1(k1)-y19(k19))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\20.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.749 0.749 0],...
'DisplayName','C 20');
x20 = x'
y20 = y'
XIN
CHARLES
plot(x20(k20),y20(k20),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.749 0.749 0]);
one20 = (sqrt((x19(k19)-x20(k20))^2 + (y19(k19)-y20(k20))^2))/34.84;
done20 = (sqrt((x1(k1)-x20(k20))^2 + (y1(k1)-y20(k20))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG
(HUANG WUXIN)\21.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.6 0.2 0],...
'DisplayName','C 21');
WU
XIN
CHARLES
x21 = x'
y21 = y'
plot(x21(k21),y21(k21),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.6 0.2 0]);
one21 = (sqrt((x20(k20)-x21(k21))^2 + (y20(k20)-y21(k21))^2))/34.84;
done21 = (sqrt((x1(k1)-x21(k21))^2 + (y1(k1)-y21(k21))^2))/34.84;
55
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\22.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'Marker','o','Color',[0.3137 0.3137 0.3137],...
'DisplayName','C 22');
x22 = x'
y22 = y'
plot(x22(k22),y22(k22),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.3137 0.3137 0.3137]);
one22 = (sqrt((x21(k21)-x22(k22))^2 + (y21(k21)-y22(k22))^2))/34.84;
done22 = (sqrt((x1(k1)-x22(k22))^2 + (y1(k1)-y22(k22))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\23.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.2471 0.2471 0.2471],...
'DisplayName','C 23');
x23 = x'
y23 = y'
plot(x23(k23),y23(k23),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.2471 0.2471 0.2471]);
one23 = (sqrt((x22(k22)-x23(k23))^2 + (y22(k22)-y23(k23))^2))/34.84;
done23 = (sqrt((x1(k1)-x23(k23))^2 + (y1(k1)-y23(k23))^2))/34.84;
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU XIN CHARLES
(HUANG WUXIN)\24.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'LineWidth',1,'Color',[0.2471 0.2471 0.2471],...
'DisplayName','C 24');
x24 = x'
y24 = y'
plot(x24(k24),y24(k24),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[0.2471 0.2471 0.2471]);
one24 = (sqrt((x23(k23)-x24(k24))^2 + (y23(k23)-y24(k24))^2))/34.84;
done24 = (sqrt((x1(k1)-x24(k24))^2 + (y1(k1)-y24(k24))^2))/34.84;
56
[z,x,y]=textread('C:\Users\User\SIM\09 2nd SEM\FYP\Normal\NG WU
(HUANG WUXIN)\25.txt','%f %f %f')
% Create plot
plot(x,y,'Parent',axes1,'Color',[1 0 0],'DisplayName','C 25');
XIN
CHARLES
x25 = x'
y25 = y'
plot(x25(k25),y25(k25),'Parent',axes1,'Marker','hexagram','LineWidth',3,'Color
',[1 0 0]);
one25 = (sqrt((x24(k24)-x25(k25))^2 + (y24(k24)-y25(k25))^2))/34.84;
done25 = (sqrt((x1(k1)-x25(k25))^2 + (y1(k1)-y25(k25))^2))/34.84;
% Create title
title('Contours Overlap');
xi=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25];
x=xi'
yi=[one1 one2 one3 one4 one5 one6 one7 one8 one9 one10 one11 one12 one13 one14
one15 one16 one17 one18 one19 one20 one21 one22 one23 one24 one25];
y=yi'
% Create axes
axes2 = axes('Parent',figure1,'Position',[0.5929 0.11 0.1788 0.815],...
'LineWidth',1);
xlim([1 25]);
box('on');
hold('all');
% Create plot
plot(x,y,'Parent',axes2,'Marker','.','LineWidth',2,'Color',[1 0 0]);
% Create title
title({'Heart Movement','Acceleration'})
yi=[done1 done2 done3 done4 done5 done6 done7 done8 done9 done10 done11 done12
done13 done14 done15 done16 done17 done18 done19 done20 done21 done22 done23
done24 done25];
y=yi'
% Create axes
axes3 = axes('Parent',figure1,'Position',[0.8165 0.11 0.1749 0.815]);
xlim([1 25]);
box('on');
hold('all');
57
% Create plot
plot(x,y,'Parent',axes3,'Marker','.','LineWidth',2);
% Create title
title({'Heart Movement','Displacement from point.'});
% Create legend
%legend1 = legend(axes1,'show');
%set(legend1,'Position',[0.01514 0.4659 0.07578 0.4911]);
58